



"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


SCREAMING_SNAKE_CASE_      						=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      						=			{
    '''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class       snake_case_    (    __snake_case					):
							__lowerCAmelCase            =       """mctct"""






							def __init__( self      ,  a_=8_0_6_5      ,  a_=1_5_3_6      ,  a_=3_6      ,  a_=6_1_4_4      ,  a_=4      ,  a_=3_8_4      ,  a_=9_2_0      ,  a_=1e-5      ,  a_=0.3      ,  a_="relu"      ,  a_=0.02      ,  a_=0.3      ,  a_=0.3      ,  a_=1      ,  a_=0      ,  a_=2      ,  a_=1      ,  a_=0.3      ,  a_=1      ,  a_=(7,)      ,  a_=(3,)      ,  a_=8_0      ,  a_=1      ,  a_=None      ,  a_="sum"      ,  a_=False      ,  **a_      ,  ):
									super().__init__(**__SCREAMING_SNAKE_CASE      ,  pad_token_id=__SCREAMING_SNAKE_CASE      ,  bos_token_id=__SCREAMING_SNAKE_CASE      ,  eos_token_id=__SCREAMING_SNAKE_CASE   )
									a_  :   str			    =  vocab_size
									a_  :   Dict			    =  hidden_size
									a_  :   Union[str, Any]			    =  num_hidden_layers
									a_  :   Any			    =  intermediate_size
									a_  :   int			    =  num_attention_heads
									a_  :   Optional[int]			    =  attention_head_dim
									a_  :   List[Any]			    =  max_position_embeddings
									a_  :   str			    =  layer_norm_eps
									a_  :   int			    =  layerdrop
									a_  :   List[str]			    =  hidden_act
									a_  :   Any			    =  initializer_range
									a_  :   str			    =  hidden_dropout_prob
									a_  :   Dict			    =  attention_probs_dropout_prob
									a_  :   List[Any]			    =  pad_token_id
									a_  :   Tuple			    =  bos_token_id
									a_  :   Union[str, Any]			    =  eos_token_id
									a_  :   Dict			    =  conv_glu_dim
									a_  :   Optional[int]			    =  conv_dropout
									a_  :   Optional[Any]			    =  num_conv_layers
									a_  :   str			    =  input_feat_per_channel
									a_  :   Any			    =  input_channels
									a_  :   List[str]			    =  conv_channels
									a_  :   Tuple			    =  ctc_loss_reduction
									a_  :   Optional[int]			    =  ctc_zero_infinity

									# prevents config testing fail with exporting to json
									a_  :   int			    =  list(__SCREAMING_SNAKE_CASE   )
									a_  :   Optional[Any]			    =  list(__SCREAMING_SNAKE_CASE   )

									if len(self.conv_kernel   ) != self.num_conv_layers:
											raise ValueError(
											    "Configuration for convolutional module is incorrect. "
											    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
											    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel   )}`, """
											    F"""`config.num_conv_layers = {self.num_conv_layers}`."""   )







"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




